> ## Documentation Index
> Fetch the complete documentation index at: https://docs.zenrows.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Topics that convert for Zenrows affiliates

> Five proven topic clusters for Zenrows affiliate content, with example angles and the funnel stage each one serves.

Five clusters, drawn from Zenrows' own search and demand research. Each one has verified search demand and a clear path from reader to customer. Use this page to choose a topic, then follow the [content guidelines](/partners/affiliates/content-guidelines) to produce it.

**Funnel stages** used below: TOFU (awareness, reader is learning), MOFU (evaluation, reader has a problem), BOFU (decision, reader is choosing a tool).

| Cluster                                                     | Funnel       | Converts because                                                   |
| ----------------------------------------------------------- | ------------ | ------------------------------------------------------------------ |
| [AI agents and MCP](#ai-agents-and-mcp)                     | TOFU to BOFU | Fast-growing demand with very little existing coverage             |
| [Integrations and workflows](#integrations-and-workflows)   | MOFU to BOFU | Reaches people already committed to a tool they want to extend     |
| [Command line and automation](#command-line-and-automation) | MOFU         | Short pieces, high intent, almost no competing content             |
| [Site-specific guides](#site-specific-guides)               | MOFU         | Highest volume and most durable traffic of any cluster             |
| [Working use cases](#working-use-cases)                     | MOFU         | Reaches decision-makers who do not yet know web data is the answer |

## AI agents and MCP

The strongest-performing cluster right now. Start from [the MCP server docs](/mcp/overview).

* How to give Claude web data access at scale with Zenrows MCP
* Setting up Zenrows MCP in Cursor, VS Code, or Windsurf
* What "agent-ready" web data actually requires
* Building a vertical AI agent with LangChain and Zenrows

## Integrations and workflows

Connecting Zenrows to a tool the reader already uses. See the [integrations section](/integrations/overview) for the shapes these take.

* Adding live web data to an n8n or Zapier workflow
* Enriching CRM records with public web data
* Feeding a RAG pipeline from live pages instead of a stale index
* Streaming collected data into a warehouse or spreadsheet

## Command line and automation

Underserved and quick to produce. Start from [the CLI docs](/cli/introduction).

* Scraping a list of URLs from your terminal with the Zenrows CLI
* Scheduling a recurring data pull with the CLI and cron
* Piping Zenrows output straight into `jq`, a CSV, or a database
* Using the CLI inside a CI job

## Site-specific guides

"How to scrape X" pieces. Pick one site, cover its specific defenses and page structure, and ship something that works.

* How to scrape Google Shopping
* How to scrape Etsy
* How to scrape Indeed
* How to scrape Idealista

## Working use cases

Business problems rather than techniques. Lower competition and reaches readers with purchasing authority.

* Build an Amazon price tracker
* Automated competitor price tracking
* Real estate data aggregation at scale
* Company data enrichment from public web sources

<Note>
  These are starting angles, not assigned titles. Adapt the wording to your own audience and keyword research.
</Note>